Comparison of two efficient methods for calculating partition functions

In the long-time pursuit of the solution to calculate the partition function (or free energy) of condensed matter, Monte-Carlo-based nested sampling should be the state-of-the-art method, and very recently, we established a direct integral approach that works at least four orders faster. In present work, the above two methods were applied to solid argon at temperatures up to $300$K, and the derived internal energy and pressure were compared with the molecular dynamics simulation as well as experimental measurements, showing that the calculation precision of our approach is about 10 times higher than that of the nested sampling method.Comment: 6 pages, 4 figure

Comparison of two efficient methods for calculating partition functions

In the long-time pursuit of the solution to calculate the partition function (or free energy) of condensed matter, Monte-Carlo-based nested sampling should be the state-of-the-art method, and very recently, we established a direct integral approach that works at least four orders faster. In present work, the above two methods were applied to solid argon at temperatures up to $300$K, and the derived internal energy and pressure were compared with the molecular dynamics simulation as well as experimental measurements, showing that the calculation precision of our approach is about 10 times higher than that of the nested sampling method.Comment: 6 pages, 4 figure

Point-of-Interest Recommendation Algorithm Based on User Similarity in Location-Based Social Networks

Location-based social network is rising recent years with the development of mobile internet, and point-of-interest (POI) recommendation is a hot topic of this field. Because the factors that affect the behavior of users are very complex, most of the research focuses on the context of the recommendation. But overall context data acquisition in practice is often difficult to obtain. In this paper, we have considered the most common collaborative recommendation algorithm based on user similarity, and discussed several methods of user similarity definition. Comparing the effect of different methods in the actual dataset, experimental results show among the factors including that social relation, check-in and geographical location the check-in is extremely important, so this work is of certain guiding significance to the actual applications

IoT Device Identification Using Device Fingerprint and Deep Learning

The foundation of security in IoT devices lies in their identity. However, traditional identification parameters, such as MAC address, IP address, and IMEI, are vulnerable to sniffing and spoofing attacks. To address this issue, this paper proposes a novel approach using device fingerprinting and deep learning for device identification. Device fingerprinting is generated by analyzing inter-arrival time (IAT), round trip time (RTT), or IAT/RTT outliers of packets used for communication in networks. We trained deep learning models, namely convolutional neural network (CNN) and CNN + LSTM (long short-term memory), using device fingerprints generated from TCP, UDP, ICMP packet types, ICMP packet type, and their outliers. Our results show that the CNN model performs better than the CNN + LSTM model. Specifically, the CNN model achieves an accuracy of 0.97 using the IAT device fingerprint of ICMP packet type, and 0.9648 using the IAT outlier device fingerprint of ICMP packet type on a publicly available dataset from the crawdad repository

Self-Addressable Memory-Based FSM: A Scalable Intrusion Detection Engine

One way to detect and thwart a network attack is to compare each incoming packet with predefined patterns, also called an attack pattern database, and raise an alert upon detecting a match. This article presents a novel pattern-matching engine that exploits a memory-based, programmable state machine to achieve deterministic processing rates that are independent of packet and pattern characteristics. Our engine is a self-addressable memory-based finite state machine (SAMFSM), whose current state coding exhibits all its possible next states. Moreover, it is fully reconfigurable in that new attack patterns can be updated easily. A methodology was developed to program the memory and logic. Specifically, we merge non-equivalent states by introducing super characters on their inputs to further enhance memory efficiency without adding labels. SAM-FSM is one of the most storage-efficient machines and reduces the memory requirement by 60 times. Experimental results are presented to demonstrate the validity of SAM-FSM

SELF-HEALING AND RECOVERING DEVICE FROM MASS STORAGE FILE SYSTEM CORRUPTION

Techniques are provided for a device to become more robust using self-healing and recovering functionality in place when operating in harsh environment. This process is seamless and transparent, and improves customer satisfaction and reduces Return Merchandise Authorization (RMA) rate